特邀编辑生物医学信号处理的机器学习技术

IF 6.7 2区医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-03 DOI:10.1109/JBHI.2024.3451828

Aceng Sambas;Sundarapandian Vaidyanathan;Azwa Abdul Aziz

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引用次数: 0

摘要

生物医学信号处理集成了对健康状况的评估，目的是提供重要的诊断信息[1]。生物医学信号处理的实时监测功能可以更好地控制慢性疾病，及时发现危险事件。在生物医学信号处理中使用云计算远程数据库对医疗保健应用具有重要意义。为了保证有效分析、最佳数据展示和产品质量，需要实施创新研究和想法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Guest Editorial Machine Learning Technology for Biomedical Signal Processing

Biomedical signal processing integrates the evaluation of health measures for the purpose of delivering significant factor diagnostic information [1]. Real-time monitoring features enabled by biomedical signal processing can lead to better chronic condition control and timely identification of hazardous occurrences. The use of a remote database for cloud computing in biomedical signal processing has significant implications for healthcare applications. To guarantee effective analysis, optimal data presentation, and product quality, innovative research and ideas need to be implemented.

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来源期刊

IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

13.60

自引率

6.50%

发文量

1151

期刊介绍： IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.

特邀编辑 生物医学信号处理的机器学习技术

摘要

特邀编辑生物医学信号处理的机器学习技术